Import Library

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from glob import glob 
from sklearn.metrics import mean_absolute_error
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.pipeline import make_pipeline
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.metrics import accuracy_score , classification_report
import pandas_profiling
from category_encoders import OneHotEncoder
from sklearn.metrics import mean_squared_error  
from sklearn.model_selection import cross_validate

t=pd.read_csv("test.csv")
t.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 418 entries, 0 to 417
Data columns (total 11 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   PassengerId  418 non-null    int64  
 1   Pclass       418 non-null    int64  
 2   Name         418 non-null    object 
 3   Sex          418 non-null    object 
 4   Age          332 non-null    float64
 5   SibSp        418 non-null    int64  
 6   Parch        418 non-null    int64  
 7   Ticket       418 non-null    object 
 8   Fare         417 non-null    float64
 9   Cabin        91 non-null     object 
 10  Embarked     418 non-null    object 
dtypes: float64(2), int64(4), object(5)
memory usage: 36.0+ KB

Import Data

def wrangel(path):
    # read data 
    df=pd.read_csv(path)
    #extract the social name
    df["title"]=df["Name"].str.extract("([A-Za-z]+)\.",expand=False)
    #convert title categorcal data 
    df.loc[df["title"]=="Mr"   , "title"]   = 0   
    df.loc[df["title"]=="Miss"   , "title"]   = 1
    df.loc[df["title"]=="Mrs"   , "title"]   = 2 
    df.loc[df["title"]=="Master"   , "title"]   = 3    
    conditions = (df["title"] == 'Ms') | (df["title"] == 'Col') | (df["title"] == 'Rev') | (df["title"] == 'Dr') | (df["title"] == 'Dona')
    df.loc[conditions, "title"] = 4
    #fill NAN Value of Fare Accorging to Social Name
    df["Fare"].fillna(df.groupby("Pclass")["Fare"].transform("median"),inplace=True)
    #fill NAN Value of Age Accorging to Social Name
    df["Age"].fillna(df.groupby("title")["Age"].transform("median"),inplace=True)
    #fill NAN Value of Embarked Accorging to Median
    df["Embarked"]=df["Embarked"].fillna("S")
    #remove nan columns
    drop=[]
    drop.append("Cabin")
    drop.append("Name")
    drop.append("Ticket")
    drop.append("title")
    df.drop(columns=drop,inplace=True)
    #convert Sex categorcal data 
    df.loc[df["Sex"]=="male"     , "Sex"]   = 0         # Male   ---> 0
    df.loc[df["Sex"]=="female"   , "Sex"]   = 1         # Female ---> 1
    #convert Embarked categorcal data 
    df.loc[df["Embarked"]=="S"   , "Embarked"]   = 0    # S ---> 1
    df.loc[df["Embarked"]=="C"   , "Embarked"]   = 1    # C ---> 2
    df.loc[df["Embarked"]=="Q"   , "Embarked"]   = 2    # Q ---> 3
    
    return df

test  =  wrangel("test.csv")
df    =  wrangel("train.csv")

df.head()

	PassengerId	Survived	Pclass	Sex	Age	SibSp	Parch	Fare	Embarked
0	1	0	3	0	22.0	1	0	7.2500	0
1	2	1	1	1	38.0	1	0	71.2833	1
2	3	1	3	1	26.0	0	0	7.9250	0
3	4	1	1	1	35.0	1	0	53.1000	0
4	5	0	3	0	35.0	0	0	8.0500	0

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 9 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   PassengerId  891 non-null    int64  
 1   Survived     891 non-null    int64  
 2   Pclass       891 non-null    int64  
 3   Sex          891 non-null    object 
 4   Age          891 non-null    float64
 5   SibSp        891 non-null    int64  
 6   Parch        891 non-null    int64  
 7   Fare         891 non-null    float64
 8   Embarked     891 non-null    object 
dtypes: float64(2), int64(5), object(2)
memory usage: 62.8+ KB

pandas_profiling.ProfileReport(df)

Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]

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df["Embarked"].value_counts()

0    646
1    168
2     77
Name: Embarked, dtype: int64

test.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 418 entries, 0 to 417
Data columns (total 8 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   PassengerId  418 non-null    int64  
 1   Pclass       418 non-null    int64  
 2   Sex          418 non-null    object 
 3   Age          418 non-null    float64
 4   SibSp        418 non-null    int64  
 5   Parch        418 non-null    int64  
 6   Fare         418 non-null    float64
 7   Embarked     418 non-null    object 
dtypes: float64(2), int64(4), object(2)
memory usage: 26.2+ KB

test.isnull().sum()

PassengerId    0
Pclass         0
Sex            0
Age            0
SibSp          0
Parch          0
Fare           0
Embarked       0
dtype: int64

Exploer Data

print("Survive :",(df["Survived"]==1).sum())
print("Deceased :",(df["Survived"]==0).sum())

Survive : 342
Deceased : 549

df.describe()

	PassengerId	Survived	Pclass	Age	SibSp	Parch	Fare
count	891.000000	891.000000	891.000000	891.000000	891.000000	891.000000	891.000000
mean	446.000000	0.383838	2.308642	29.393008	0.523008	0.381594	32.204208
std	257.353842	0.486592	0.836071	13.269209	1.102743	0.806057	49.693429
min	1.000000	0.000000	1.000000	0.420000	0.000000	0.000000	0.000000
25%	223.500000	0.000000	2.000000	21.000000	0.000000	0.000000	7.910400
50%	446.000000	0.000000	3.000000	30.000000	0.000000	0.000000	14.454200
75%	668.500000	1.000000	3.000000	35.000000	1.000000	0.000000	31.000000
max	891.000000	1.000000	3.000000	80.000000	8.000000	6.000000	512.329200

# Create the pie chart
values=df["Survived"].value_counts()
label=["Deceased ","Survive "]
plt.pie(values, labels=label,autopct='%1.1f%%')
# Add a title
plt.title('Distribution of Survived')
# Display the chart
plt.show()

plt.hist(df["Parch"],bins=5, edgecolor='black');
plt.xlabel('Values')
plt.ylabel('Frequancy')
plt.title("Values of Parch")
plt.show();

survive=df[df["Survived"]==1]["SibSp"].value_counts()
death=df[df["Survived"]==0]["SibSp"].value_counts()
dx=pd.DataFrame([survive,death],index=["survive","death"])
dx.plot(kind="bar");
plt.title("Survive of SibSp ");

survive=df[df["Survived"]==1]["Pclass"].value_counts()
death=df[df["Survived"]==0]["Pclass"].value_counts()
dx=pd.DataFrame([survive,death],index=["survive","death"])
dx.plot(kind="bar");
plt.title("Survive of Pclass ");

class1=df[df["Pclass"]==1]["Embarked"].value_counts()
class2=df[df["Pclass"]==2]["Embarked"].value_counts()
class3=df[df["Pclass"]==3]["Embarked"].value_counts()
dx=pd.DataFrame([class1,class2,class3],index=["class 1","class 2","class 3"])
dx.plot(kind="bar",stacked=True);
plt.title("Survive of Pclass ");

We Found that Embarked from S in 1st & 2nd & 3rd Class

# Create the pie chart
values=df["Sex"].value_counts()
label=["male","female"]
plt.pie(values, labels=label,autopct='%1.1f%%')
# Add a title
plt.title('Distribution of Survived')
# Display the chart
plt.show()

survive = df[df["Survived"]==1]["Sex"].value_counts()
death   = df[df["Survived"]==0]["Sex"].value_counts()
dx      = pd.DataFrame([survive,death],index=["survive","death"])
dx=dx.rename(columns={0:"male",1:"female"})
dx.plot(kind="bar")
plt.legend()
plt.title("Survive of Sex");

corrleation = df.drop(columns="Survived").corr()
sns.heatmap(corrleation)

<AxesSubplot:>

Split Data

df

	PassengerId	Survived	Pclass	Sex	Age	SibSp	Parch	Fare	Embarked
0	1	0	3	0	22.0	1	0	7.2500	0
1	2	1	1	1	38.0	1	0	71.2833	1
2	3	1	3	1	26.0	0	0	7.9250	0
3	4	1	1	1	35.0	1	0	53.1000	0
4	5	0	3	0	35.0	0	0	8.0500	0
...	...	...	...	...	...	...	...	...	...
886	887	0	2	0	27.0	0	0	13.0000	0
887	888	1	1	1	19.0	0	0	30.0000	0
888	889	0	3	1	21.0	1	2	23.4500	0
889	890	1	1	0	26.0	0	0	30.0000	1
890	891	0	3	0	32.0	0	0	7.7500	2

891 rows × 9 columns

target="Survived"
y = df[target]
X = df.drop(columns=target)
x_train , x_test , y_train , y_test = train_test_split(X,y,test_size=0.2,random_state=42)
print("X_train shape:", x_train.shape)
print("y_train shape:", y_train.shape)
print("X_test shape:", x_test.shape)
print("y_test shape:", y_test.shape)

X_train shape: (712, 8)
y_train shape: (712,)
X_test shape: (179, 8)
y_test shape: (179,)

Baseline

y_train_mean = y_train.mean()
print ("Baseline :",round(y_train_mean,2))

Baseline : 0.38

Logestic Regression

Itrate

log_model = LogisticRegression(max_iter=10000)

log_model.fit(x_train,y_train)

LogisticRegression(max_iter=10000)

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Evaluate

accuracy=classification_report(y_test,log_model.predict(x_test))
print(accuracy)

              precision    recall  f1-score   support

           0       0.82      0.85      0.83       105
           1       0.77      0.73      0.75        74

    accuracy                           0.80       179
   macro avg       0.79      0.79      0.79       179
weighted avg       0.80      0.80      0.80       179

acc_test = accuracy_score(y_test,log_model.predict(x_test))
acc_test = accuracy_score(y_test,log_model.predict(x_test))
acc_train= accuracy_score(y_train,log_model.predict(x_train))
print("Accuracy test:",round(acc_test,2))
print("Accuracy train:",round(acc_train,2))

Accuracy test: 0.8
Accuracy train: 0.8

KNN Classfier

knn= KNeighborsClassifier(n_neighbors=13)
knn.fit(x_train,y_train)

KNeighborsClassifier(n_neighbors=13)

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accuracy=classification_report(y_test,knn.predict(x_test))
print(accuracy)

              precision    recall  f1-score   support

           0       0.63      0.90      0.75       105
           1       0.66      0.26      0.37        74

    accuracy                           0.64       179
   macro avg       0.64      0.58      0.56       179
weighted avg       0.64      0.64      0.59       179

scoring="accuracy"
score = cross_validate(knn , x_train.drop(columns=["PassengerId"],axis=1),y_train,cv=k_fold, n_jobs=1,scoring=scoring)
print(score['test_score'])

[0.72222222 0.69444444 0.71830986 0.63380282 0.70422535 0.78873239
 0.71830986 0.69014085 0.74647887 0.66197183]

print("Accuracy :",round(np.mean(score['test_score']),2))

Accuracy : 0.71

Descion Tree

# Create a decision tree classifier
dec_tree=  DecisionTreeClassifier()
# Train the classifier
dec_tree.fit(x_train, y_train)

DecisionTreeClassifier()

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accuracy=classification_report(y_test,dec_tree.predict(x_test))
print(accuracy)

              precision    recall  f1-score   support

           0       0.79      0.76      0.78       105
           1       0.68      0.72      0.70        74

    accuracy                           0.74       179
   macro avg       0.74      0.74      0.74       179
weighted avg       0.75      0.74      0.74       179

acc_test = accuracy_score(y_test,dec_tree.predict(x_test))
print("Accuracy test:",round(acc_test,2))

Accuracy test: 0.74

scoring="accuracy"
score = cross_validate(dec_tree , x_train.drop(columns=["PassengerId"],axis=1),y_train,cv=k_fold, n_jobs=1,scoring=scoring)
print("Accuracy :",round(np.mean(score['test_score']),2))

Accuracy : 0.78

Random Forest

# Create a Random Forest classifier
rf_classifier = RandomForestClassifier()
# Train the classifier
rf_classifier.fit(x_train, y_train)

RandomForestClassifier()

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# Calculate the accuracy
accuracy = accuracy_score(y_test, rf_classifier.predict(x_test))
print("Accuracy:", round(accuracy,2))

Accuracy: 0.84

scoring="accuracy"
score = cross_validate(rf_classifier , x_train.drop(columns=["PassengerId"],axis=1),y_train, n_jobs=1,scoring=scoring)
print("Accuracy :",round(np.mean(score['test_score']),1))

Accuracy : 0.8

Naive Bayes

nav= GaussianNB()
# Train the classifier
nav.fit(x_train, y_train)

GaussianNB()

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# Calculate the accuracy
accuracy = accuracy_score(y_test, nav.predict(x_test))
print("Accuracy:", round(accuracy,2))

Accuracy: 0.78

scoring="accuracy"
score = cross_validate(nav , x_train.drop(columns=["PassengerId"],axis=1),y_train, n_jobs=1,scoring=scoring)
print("Accuracy :",round(np.mean(score['test_score']),2))

Accuracy : 0.8

Communicat

The best model is Random Forest with Accuracy : 82

pred_test=rf_classifier.predict(test)

data = pd.DataFrame({'PassengerId': test["PassengerId"], 'Survived': pred_test})

data.head()

	PassengerId	Survived
0	892	0
1	893	0
2	894	0
3	895	0
4	896	0

data.to_csv(r'D:\projects\gender_submission.csv', index=False)